Multivariate radial symmetry of copula functions: finite sample comparison in the i.i.d case
Given a d-dimensional random vector X = (X1, . . ., Xd), if the standard uniform vector U obtained by the component-wise probability integral transform (PIT) of X has the same distribution of its point reflection through the center of the unit hypercube, then X is said to have copula radial symmetry...
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| Main Authors: | , , |
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| Format: | article |
| Language: | EN |
| Published: |
De Gruyter
2021
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| Subjects: | |
| Online Access: | https://doaj.org/article/92dd703a094d44d6afd26f3c9ae087b4 |
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| Summary: | Given a d-dimensional random vector X = (X1, . . ., Xd), if the standard uniform vector U obtained by the component-wise probability integral transform (PIT) of X has the same distribution of its point reflection through the center of the unit hypercube, then X is said to have copula radial symmetry. We generalize to higher dimensions the bivariate test introduced in [11], using three different possibilities for estimating copula derivatives under the null. In a comprehensive simulation study, we assess the finite-sample properties of the resulting tests, comparing them with the finite-sample performance of the multivariate competitors introduced in [17] and [1]. |
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